Vignette 1: Getting Started with BANDLE
14 December 2021
Abstract
This vignette provides an introduction to the BANDLE package (Crook et al. 2021) and follows a short theortical example of how to perform differential localisation analysis of quantitative proteomics data using the BANDLE model. Explanation and general recommendations of the input parameters are provided here. For a more comprehensive workflow which follows a real-life use case, please see the second vignette in this package.
Package
bandle 1.0
1 Introduction
Bayesian ANalysis of Differential Localisation Experiments (BANDLE) is an integrative semi-supervised functional mixture model, developed by (Crook et al. 2021), to obtain the probability of a protein being differentially localised between two conditions.
In this vignette we walk users through how to install and use the R (R Development Core Team 2011)
Bioconductor (Gentleman et al. 2004) bandle package
by simulating a well-defined differential localisation experiment from spatial
proteomics data from the pRolocdata package (Gatto et al. 2014).
The BANDLE method uses posterior Bayesian computations performed using Markov-chain Monte-Carlo (MCMC) and thus uncertainty estimates are available (Gilks, Richardson, and Spiegelhalter 1995). Throughout this vignette we use the term differentially localised to pertain to proteins which are assigned to different sub-cellular localisations between two conditions. One of the main outputs of BANDLE is the probability that a protein is differentially localised between two conditions.
2 The data
In this vignette and (Crook et al. 2021), the main data source that we use to study
differential protein sub-cellular localisation are data from high-throughput
mass spectrometry-based experiments. The data from these types of experiments
traditionally yield a matrix of measurements wherein we have, for example, PSMs,
peptides or proteins along the rows, and samples/channels/fractions along the
columns. The bandle package uses the MSnSet class as implemented in the
Bioconductor MSnbase package and thus requires users to import
and store their data as a MSnSet instance. For more details on how to create a
MSnSet see the relevant vignettes in pRoloc. There is also
additional information and examples in the pRoloc sister package.
The pRolocdata experiment data package is a good starting
place to look for test data. This data package contains tens of quantitative
proteomics experiments, stored as MSnSets.
2.1 A well-defined theoretical example
We are going to generate a differential localisation experiment. They key elements are replicates, and a perturbation of interest. There is code within the bandle package to simulate an example experiment.
In the code chunk below we begin by loading the pRolocdata
package to obtain a spatial proteomics dataset. This will be the basis of our
simulation which will use boostrapping to generate new datasets. The dataset we
have chosen to load is a dataset from 2009 (tan2009r1). This is data from a
early LOPIT experiment performed on Drosophila embryos (Tan et al. 2009). The aim of
this experiment was to apply LOPIT to an organism with heterogeneous cell types.
This experiment used four isotopes across four distinct fractions and thus
yielded four measurements (features) per protein profile.
library("pRolocdata")
data("tan2009r1")
plot2D(tan2009r1,
main = "An example spatial proteomics datasets",
grid = FALSE)
addLegend(tan2009r1, where = "topleft", cex = 0.7, ncol = 2)
The following code chuck simulates a differential localisation experiment. It
will generate numRep/2 of each a control and treatment condition. We will also
simulate relocalisations for numDyn proteins.
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1,
numRep = 6,
numDyn = 100)The list of the 6 simulated experiments are found in tansim$lopitrep. Each one
is an MSnSet instance (the standard data container for proteomics experimental
data). The first 3 are the simulated control experiments (see
tansim$lopitrep[1:3]), and the following 3 in the list are the treatment
condition simulated experiments (see tansim$lopitrep[4:6]). We can plot them
using the plot2D function from pRoloc.
plot_title <- c(paste0("Replicate ", seq(3), " condition", " A"),
paste0("Replicate ", seq(3), " condition", " B"))
par(mfrow = c(2, 3))
out <- lapply(seq(tansim$lopitrep), function(z)
plot2D(tansim$lopitrep[[z]], grid = FALSE, main = plot_title[z]))
For understanding, exploring and visualizing individual spatial proteomics
experiments, see the vignettes in pRoloc and MSnbase packages.
tansim$lopitrep[[1]]## MSnSet (storageMode: lockedEnvironment)
## assayData: 888 features, 4 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: X114 X115 X116 X117
## varLabels: Fractions
## varMetadata: labelDescription
## featureData
## featureNames: P20353 P53501 ... P07909 (888 total)
## fvarLabels: FBgn Protein.ID ... knn.scores (18 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 19317464
## Annotation:
## - - - Processing information - - -
## Added markers from 'mrk' marker vector. Thu Jul 16 22:53:44 2015
## Performed knn prediction (k=10) Tue Dec 14 23:52:16 2021
## MSnbase version: 1.17.123 Preparing for bandle analysis
The main function of the package is bandle, this uses a complex model
to analyse the data. Markov-Chain Monte-Carlo (MCMC) is used to sample the
posterior distribution of parameters and latent variables. From which statistics
of interest can be computed. Here we only run a few iterations for brevity but
typically one needs to run thousands of iterations to ensure convergence, as
well as multiple parallel chains.
3.1 Fitting Gaussian processes
First, we need to fit non-parametric regression functions to the markers
profiles, upon which we place our analysis. This uses Gaussian processes. The
fitGPmaternPC function can be used and fits some default penalised complexity
priors (see ?fitGP), which work well. However, these can be altered, which is
demonstrated in the next code chunk
par(mfrow = c(3,4))
gpParams <- lapply(tansim$lopitrep, function(x) fitGPmaternPC(x))
We apply the fitGPmaternPC function to each datasets by calling lapply over
the tansim$lopitrep list of datasets. The output of fitGPmaternPC returns a
list of posterior predictive means and standard deviations. As well as MAP
hyperparamters for the GP. As a side effect a profile plot is produced for each
class in each replicate condition where the posterior predictive distributions
are overlayed with markers protein profiles.
The prior needs to form a K*3 matrix. K corresponds to the number of
subcellular classes in the data, and 3 columns for (1) the prior, (2)
length-scale amplitude and (3) standard deviation parameters (see hyppar in
?fitGP). Increasing these values, increases the shrinkage. For more details
see the manuscript by Crook et al. (2021).
K <- length(getMarkerClasses(tansim$lopitrep[[1]], fcol = "markers"))
pc_prior <- matrix(NA, ncol = 3, K)
pc_prior[seq.int(1:K), ] <- matrix(rep(c(10, 60, 250),
each = K), ncol = 3)Now we have generated these complexity priors we can pass them as an
argument to the fitGPmaternPC function. For example,
par(mfrow = c(3,4))
gpParams <- lapply(tansim$lopitrep,
function(x) fitGPmaternPC(x, hyppar = pc_prior))
By looking at the plot of posterior predictives we can see the GP fit looks sensible.
3.2 Setting the prior on the weights
The next step is to set up the matrix Dirichlet prior on the mixing weights. These
weights are defined across datasets so these are slightly different to mixture
weights in usual mixture models. The \((i,j)^{th}\) entry is the prior probability
that a protein localises to organelle \(i\) in the control and \(j\) in the treatment.
This mean that off-diagonal terms have a different interpretation to diagonal terms.
Since we expect re-localisation to be rare, off-diagonal terms should be small.
The following functions help set up the priors and how to interpret them. The
parameter q allow us to check the prior probability that more than q
differential localisations are expected.
set.seed(1)
dirPrior = diag(rep(1, K)) + matrix(0.001, nrow = K, ncol = K)
predDirPrior <- prior_pred_dir(object = tansim$lopitrep[[1]],
dirPrior = dirPrior,
q = 15)The mean number of re-localisations is small:
predDirPrior$meannotAlloc## [1] 0.3515246The prior probability that more than q differential localisations are
expected is small
predDirPrior$tailnotAlloc## [1] 6e-04The full prior predictive can be visualised as histogram. The prior probability that proteins are allocated to different components between datasets concentrates around 0.
hist(predDirPrior$priornotAlloc, col = getStockcol()[1])
If the the distribution did not concentrated around zero, as in most use-cases
we would expect the number of re-localisations to be small, users could try
testing smaller values in dirPrior for example, 0.0005 or smaller, instead of
0.001.
4 Running the bandle function
We are now ready to run the main bandle function. Remember to carefully
select the datasets and replicates that define the control and treatment.
As a reminder, in this “Getting started” vignette we have used a small dataset
and generated theoretical triplicates of each theoretical condition. Please see
the second vignette in this package for a more detailed workflow and real
biological use-case. In the below code chunk we run bandle for only 50
iterations for the convenience of building the vignette, but typically we’d
recommend you run the number of iterations (numIter) in the \(1000\)s.
Remember: the first 3 datasets are the first 3 elements of tansim and the
final 3 datasets are the treatment triplicates.
control <- tansim$lopitrep[1:3]
treatment <- tansim$lopitrep[4:6]
bandleres <- bandle(objectCond1 = control,
objectCond2 = treatment,
numIter = 50, # usually 10,000
burnin = 5, # usually 5,000
thin = 1, # usually 20
gpParams = gpParams,
pcPrior = pc_prior,
numChains = 1, # usually >=4
dirPrior = dirPrior)The bandle function generates an object of class bandleParams. The show
method indicates the number of parallel chains that were run, this should
typically be greater than 4 (here we use 1 just as a demo).
bandleres## Object of class "bandleParams"
## Method: bandle
## Number of chains: 15 Analysing bandle output
Before we can begin to extract protein allocation information and a list of
proteins which are differentially localised between conditions, we first need to
populate the bandleres object by calling the bandleProcess function.
5.1 Populating a bandleres object
Currently, the summary slots of the bandleres object are empty. The
summaries function accesses them.
summaries(bandleres)## [[1]]
## An object of class "bandleSummary"
## Slot "posteriorEstimates":
## DataFrame with 0 rows and 0 columns
##
## Slot "diagnostics":
## <0 x 0 matrix>
##
## Slot "bandle.joint":
## <0 x 0 matrix>
##
##
## [[2]]
## An object of class "bandleSummary"
## Slot "posteriorEstimates":
## DataFrame with 0 rows and 0 columns
##
## Slot "diagnostics":
## <0 x 0 matrix>
##
## Slot "bandle.joint":
## <0 x 0 matrix>These can be populated as follows
bandleres <- bandleProcess(bandleres)These slot have now been populated
summary(summaries(bandleres))## Length Class Mode
## [1,] 1 bandleSummary S4
## [2,] 1 bandleSummary S45.1.1 bandle results
We can save the results by calling summaries. We see that it is
of length 2. 1 for control and 1 for treatment.
res <- summaries(bandleres)
length(res)## [1] 2There are a number of slots,
str(res[[1]])## Formal class 'bandleSummary' [package "bandle"] with 3 slots
## ..@ posteriorEstimates:Formal class 'DFrame' [package "S4Vectors"] with 6 slots
## .. .. ..@ rownames : chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. ..@ nrows : int 677
## .. .. ..@ listData :List of 7
## .. .. .. ..$ bandle.allocation : Named chr [1:677] "PM" "Golgi" "Ribosome 40S" "PM" ...
## .. .. .. .. ..- attr(*, "names")= chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. .. ..$ bandle.probability : Named num [1:677] 1 0.994 1 0.968 0.996 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. .. ..$ bandle.outlier : num [1:677] 0 0 0 0 0 0 0 0 0 0 ...
## .. .. .. ..$ bandle.probability.lowerquantile: Named num [1:677] 1 0.979 1 0.896 0.983 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. .. ..$ bandle.probability.upperquantile: Named num [1:677] 1 0.999 1 1 1 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. .. ..$ bandle.mean.shannon : Named num [1:677] 0.00 0.00 5.74e-08 0.00 7.71e-04 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. .. ..$ bandle.differential.localisation: Named num [1:677] 0 0 0 0.0667 0 ...
## .. .. .. .. ..- attr(*, "names")= chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. ..@ elementType : chr "ANY"
## .. .. ..@ elementMetadata: NULL
## .. .. ..@ metadata : list()
## ..@ diagnostics : logi [1, 1] NA
## ..@ bandle.joint : num [1:677, 1:11] 1.27e-100 1.45e-140 1.10e-17 8.40e-136 4.09e-56 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:677] "P20353" "P53501" "Q7KU78" "P04412" ...
## .. .. ..$ : chr [1:11] "Cytoskeleton" "ER" "Golgi" "Lysosome" ...The main one of interest is the posteriorEstimates slot,
res[[1]]@posteriorEstimates## DataFrame with 677 rows and 7 columns
## bandle.allocation bandle.probability bandle.outlier
## <character> <numeric> <numeric>
## P20353 PM 0.999998 0
## P53501 Golgi 0.994177 0
## Q7KU78 Ribosome 40S 1.000000 0
## P04412 PM 0.968461 0
## Q7KJ73 Ribosome 60S 0.996389 0
## ... ... ... ...
## Q95TL8 ER 1.000000 0
## P25007 Proteasome 0.999999 0
## P41374 Cytoskeleton 0.999975 0
## Q8SZM1 Peroxisome 0.863698 0
## P07909 Nucleus 1.000000 0
## bandle.probability.lowerquantile bandle.probability.upperquantile
## <numeric> <numeric>
## P20353 0.999995 1.000000
## P53501 0.979071 0.999196
## Q7KU78 1.000000 1.000000
## P04412 0.895974 1.000000
## Q7KJ73 0.982639 1.000000
## ... ... ...
## Q95TL8 1.000000 1.000000
## P25007 0.999994 1.000000
## P41374 0.999924 0.999998
## Q8SZM1 0.526096 0.992823
## P07909 1.000000 1.000000
## bandle.mean.shannon bandle.differential.localisation
## <numeric> <numeric>
## P20353 0.00000e+00 0.0000000
## P53501 0.00000e+00 0.0000000
## Q7KU78 5.73779e-08 0.0000000
## P04412 0.00000e+00 0.0666667
## Q7KJ73 7.71039e-04 0.0000000
## ... ... ...
## Q95TL8 0.00000e+00 0.000000
## P25007 3.95029e-07 0.000000
## P41374 0.00000e+00 0.000000
## Q8SZM1 7.38636e-03 0.866667
## P07909 2.28152e-07 0.000000This output object is a data.frame containing the protein allocations and
associated localisation probabilities (including the upper and lower quantiles
of the allocation probability distribution), the mean Shannon entropy and the
bandle.differential.localisation probability.
5.2 Extracting posteriors and allocation results
We create two new objects pe1 and pe2 in the below code chunk which contain
the output of the posteriorEstimates slot.
pe1 <- res[[1]]@posteriorEstimates
pe2 <- res[[2]]@posteriorEstimatesOne quantity of interest is the protein allocations, which we can plot as a barplot.
alloc1 <- pe1$bandle.allocation
alloc2 <- pe2$bandle.allocation
par(mfrow = c(1, 2), oma = c(6,2,2,2))
barplot(table(alloc1), col = getStockcol()[2],
las = 2, main = "Protein allocation: control")
barplot(table(alloc2), col = getStockcol()[2],
las = 2, main = "Protein allocation: treatment")
The barplot tells us for this example that bandle has allocated the majority
of unlabelled proteins to the ER, followed by the Golgi (irrespective of the
posterior probability).
The associated posterior estimates are located in the bandle.probability
column.
pe_alloc1 <- pe1$bandle.probability
pe_alloc2 <- pe1$bandle.probability5.3 Allocation probabilities
The full allocation probabilities are stored in the tagm.joint slot. These can
be visualised in a heatmap
bjoint_control <- summaries(bandleres)[[1]]@bandle.joint
pheatmap(bjoint_control, cluster_cols = FALSE, color = viridis(n = 25))
bjoint_treatment <- summaries(bandleres)[[2]]@bandle.joint
pheatmap(bjoint_treatment, cluster_cols = FALSE, color = viridis(n = 25))
5.4 Predicting the subcellular location
We can append the results to our original MSnSet datasets using the
bandlePredict function.
xx <- bandlePredict(control,
treatment,
params = bandleres,
fcol = "markers")
res_control <- xx[[1]]
res_treatment <- xx[[2]]The output is a list of MSnSets. In this example,
we have 3 for the control and 3 for the treatment.
length(res_control)## [1] 3length(res_treatment)## [1] 3The results are appended to the first MSnSet feature data slot
for each condition.
fvarLabels(res_control[[1]])## [1] "FBgn" "Protein.ID"
## [3] "Flybase.Symbol" "AccessionNo"
## [5] "EntryName" "AccessionNoAll"
## [7] "EntryNameAll" "No.peptide.IDs"
## [9] "Mascot.score" "No.peptide.quantified"
## [11] "PLSDA" "pd.2013"
## [13] "pd.markers" "markers.orig"
## [15] "markers" "markers.tl"
## [17] "knn" "knn.scores"
## [19] "bandle.allocation" "bandle.probability"
## [21] "bandle.probability.lowerquantile" "bandle.probability.upperquantile"
## [23] "bandle.mean.shannon" "bandle.differential.localisation"
## [25] "bandle.outlier" "bandle.joint"To access them use the fData function
fData(res_control[[1]])$bandle.probability
fData(res_control[[1]])$bandle.allocation5.4.1 Thresholding on protein allocations
It is common practice in supervised machine learning to set a specific threshold
on which to define new assignments/allocations, below which classifications are
left unassigned/unknown. Indeed, we do not expect the whole subcellular
diversity to be represented by the 11 niches defined here, we expect there to be
many more, many of which will be multiply localised within the cell. It is
important to allow for the possibility of proteins to reside in multiple
locations (this information is available in the bandle.joint slot - see above
for more details on multiple location).
As we are using a Bayesian model the outputs of the classifier are probabilities. This not only allows us to look at the distribution of probabilities over all subcellular classes but also allows us to extract a probability threshold on which we can define new assignments.
The subcellular allocations are located in the bandle.allocation column of the
fData slot and the posteriors are located in the bandle.probability slot. We
can use the getPredictions function from the pRoloc package to return a set
of predicted localisations according to if they meet a probability threshold.
For example, in the below code chunk we set a 1% FDR for assigning proteins a subcellular nice, below which we leave them unlabelled.
res_control[[1]] <- getPredictions(res_control[[1]],
fcol = "bandle.allocation",
scol = "bandle.probability",
mcol = "markers",
t = .99)## ans
## Cytoskeleton ER Golgi Lysosome mitochondrion
## 12 220 133 9 84
## Nucleus Peroxisome PM Proteasome Ribosome 40S
## 40 7 134 45 74
## Ribosome 60S unknown
## 50 80res_treatment[[1]] <- getPredictions(res_treatment[[1]],
fcol = "bandle.allocation",
scol = "bandle.probability",
mcol = "markers",
t = .99)## ans
## Cytoskeleton ER Golgi Lysosome mitochondrion
## 17 192 121 15 84
## Nucleus Peroxisome PM Proteasome Ribosome 40S
## 48 14 137 50 71
## Ribosome 60S unknown
## 49 90We may also wish to take into account the probability of the protein being an
outlier and thus use the results in the bandle.outlier column of the feature
data. We could calculate the product of the posterior and the outlier (as they
are both probabilities) to obtain a localisation score that takes into account
the outlier model. More details on this are found in the second vignette of this
package.
5.5 Differential localisation probability
As previously mentioned the term “differentially localised” is used to pertain to proteins which are assigned to different sub-cellular localisations between two conditions. For the majority of users this is the main output they are keen to extract using the BANDLE method.
Following on from the above example, after extracting posterior estimates for
each condition using the summaries function we can also access the
differential localisation probability as it is stored in the
bandle.differential.localisation column of the data.frames of pe1 and
pe2, in the above sections.
The differential localisation probability tells us which proteins are most likely to differentially localise. If we take a 1% FDR and examine how many proteins get a differential probability greater than 0.99 we find there are 74 proteins above this threshold.
diffloc_probs <- pe1$bandle.differential.localisation
head(diffloc_probs, 50)## P20353 P53501 Q7KU78 P04412 Q7KJ73 Q9VM65 Q9VCK0
## 0.00000000 0.00000000 0.00000000 0.06666667 0.00000000 0.00000000 0.00000000
## B7Z0W3 Q9V415 Q00174 Q9V769 Q27593 Q9V780 P19109
## 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## Q95NR4 O77047 Q8SXD0 Q9VBV5 Q9VBU5 Q8IA62 Q9Y105
## 0.00000000 0.04444444 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## Q9W2M4 P26308 Q9VP77 Q5U0Z2 Q960E2 Q9VLJ6 Q9VJ39
## 0.00000000 1.00000000 1.00000000 0.00000000 0.06666667 1.00000000 0.95555556
## Q9VTX8 Q9VTZ5 B7Z0C1 Q9VRJ4 M9PCB7 P46150 A1ZBH5
## 0.00000000 0.35555556 0.00000000 0.00000000 0.00000000 0.06666667 0.00000000
## Q9W3M7 A8DZ29 Q9VN14 Q9VZL3 M9PC99 Q86BP3 Q9W3N9
## 0.00000000 0.44444444 0.11111111 0.00000000 0.02222222 0.00000000 0.00000000
## Q7K5M6 P16620 P48375 Q9VMB9 Q9VI55 Q9VU58 Q9V498
## 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.22222222 0.00000000
## Q9V496
## 1.00000000length(which(diffloc_probs[order(diffloc_probs, decreasing = TRUE)] > 0.99))## [1] 76This can also be seen on a rank plot
plot(diffloc_probs[order(diffloc_probs, decreasing = TRUE)],
col = getStockcol()[3], pch = 19, ylab = "Probability",
xlab = "Rank", main = "Differential localisation rank plot")
In-line with our expectations, the rank plot indicates that most proteins are not differentially localised.
5.5.1 Estimating uncertainty
5.5.1.1 Applying the bootstrapdiffLocprob function
We can examine the top n proteins (here we use an example of top = 100) and
produce bootstrap estimates of the uncertainty (note here the uncertainty is
likely to be underestimated as we did not produce many MCMC samples). These can
be visualised as ranked boxplots.
set.seed(1)
boot_t <- bootstrapdiffLocprob(params = bandleres, top = 100,
Bootsample = 5000, decreasing = TRUE)
boxplot(t(boot_t), col = getStockcol()[5],
las = 2, ylab = "Probability", ylim = c(0, 1),
main = "Differential localisation \nprobability plot (top 100 proteins)")
5.5.1.2 Applying the binomDiffLoc function
Instead of applying the bootstrapdiffLocprob we could use the binomDiffLoc
function to obtain credible intervals from the binomial distribution.
bin_t <- binomialDiffLocProb(params = bandleres, top = 100,
nsample = 5000, decreasing = TRUE)
boxplot(t(bin_t), col = getStockcol()[5],
las = 2, ylab = "Probability", ylim = c(0, 1),
main = "Differential localisation \nprobability plot (top 100 proteins)")
5.5.1.3 Obtaining probability estimates
There are many ways we could obtain probability estimates from either of the above methods. We could, for example, take the mean of each protein estimate, or compute the cumalative error (there is not really a false discovery rate in Bayesian statistics) or we could threshold on the interal to reduce the number of differential localisations if you feel the model has been overconfident.
# more robust estimate of probabilities
dprobs <- rowMeans(bin_t)
# compute cumalative error, there is not really a false discovery rate in
# bayesian statistics but you can look at the cummatlive error rate
ce <- cumsum(1 - dprobs)
# you could threshold on the interval and this will reduce the number of
# differential localisations
qt <- apply(bin_t, 1, function(x) quantile(x, .025))6 Visualising differential localisation
We can visualise the changes in localisation between conditions on an alluvial
plot using the plotTranslocations function
plotTranslocations(bandleres)
By default, irrespective of probability, the predicted allocation is taken from
Or alternatively, on a chord (circos) diagram
plotTranslocations(bandleres, type = "chord")
Lastly, we can also pass the argument table = TRUE to the plotTranslocations
function to display a summary table of the number of proteins that have changed
in location between conditions
(sum.res <- plotTranslocations(bandleres, table = TRUE))## Condition1 Condition2 value
## 11 ER Cytoskeleton 2
## 12 ER Golgi 1
## 13 ER Lysosome 6
## 14 ER mitochondrion 1
## 15 ER Nucleus 4
## 16 ER Peroxisome 7
## 17 ER PM 4
## 18 ER Proteasome 5
## 19 ER Ribosome 40S 1
## 22 Golgi ER 1
## 24 Golgi mitochondrion 5
## 25 Golgi Nucleus 3
## 27 Golgi PM 1
## 29 Golgi Ribosome 40S 1
## 30 Golgi Ribosome 60S 4
## 41 mitochondrion Cytoskeleton 2
## 43 mitochondrion Golgi 2
## 45 mitochondrion Nucleus 1
## 56 Nucleus Peroxisome 1
## 63 Peroxisome Golgi 5
## 71 PM Cytoskeleton 1
## 72 PM ER 1
## 73 PM Golgi 3
## 78 PM Proteasome 6
## 82 Proteasome ER 1
## 85 Proteasome mitochondrion 1
## 86 Proteasome Nucleus 1
## 88 Proteasome PM 3
## 89 Proteasome Ribosome 40S 1
## 90 Proteasome Ribosome 60S 3
## 91 Ribosome 40S Cytoskeleton 1
## 93 Ribosome 40S Golgi 5
## 95 Ribosome 40S mitochondrion 2
## 96 Ribosome 40S Nucleus 1
## 99 Ribosome 40S Proteasome 1
## 100 Ribosome 40S Ribosome 60S 3
## 109 Ribosome 60S Proteasome 4
## 110 Ribosome 60S Ribosome 40S 16.0.1 Important consideration
These visualisations are showing the change in class label between the two
conditions (as assigned by bandle i.e. the result stored in
bandle.allocation). The results are taken directly from bandleres and thus
no thresholding on the class label and posterior to allow for proteins to be
left “unassigned” or unknown, is conducted. Furthermore, there is not
thresholding on the bandle.differential.localisation probability.
It would be better to re-plot these figures to get a better representation of
what is moving. The easiest way to do this is to pass the MSnSets output after
performing bandlePredict and getPredictions.
For example, first let’s identify which proteins get a high differential localisation probability,
ind <- which(fData(res_control[[1]])$bandle.differential.localisation > 0.99)
res_dl_control <- res_control[[1]][ind, ]
res_dl_treatment <- res_treatment[[1]][ind, ]Now we can plot only these and also specify where the prediction results are located,
mycols <- c(getStockcol()[seq(getMarkerClasses(res_control[[1]]))], "grey")
names(mycols) <- c(getMarkerClasses(res_control[[1]]), "unknown")
plotTranslocations(list(res_dl_control, res_dl_treatment),
fcol = "bandle.allocation.pred", col = mycols)## 76 features in common## ------------------------------------------------
## If length(fcol) == 1 it is assumed that the
## same fcol is to be used for both datasets
## setting fcol = c(bandle.allocation.pred,bandle.allocation.pred)
## ----------------------------------------------
(final.res <- plotTranslocations(params = list(res_dl_control, res_dl_treatment),
fcol = "bandle.allocation.pred", table = TRUE))## Condition1 Condition2 value
## 1 ER Golgi 1
## 2 ER mitochondrion 1
## 3 ER Nucleus 4
## 4 ER Peroxisome 7
## 5 ER PM 4
## 6 ER Proteasome 4
## 7 ER Ribosome 40S 1
## 9 ER unknown 1
## 10 ER Cytoskeleton 1
## 11 ER Lysosome 6
## 12 Golgi ER 1
## 13 Golgi mitochondrion 3
## 14 Golgi Nucleus 2
## 18 Golgi Ribosome 40S 1
## 20 Golgi unknown 2
## 25 mitochondrion Nucleus 1
## 31 mitochondrion unknown 1
## 32 mitochondrion Cytoskeleton 1
## 37 Nucleus Peroxisome 1
## 46 Peroxisome Golgi 2
## 56 PM ER 1
## 57 PM Golgi 1
## 61 PM Proteasome 5
## 64 PM unknown 1
## 67 Proteasome ER 1
## 69 Proteasome mitochondrion 1
## 70 Proteasome Nucleus 1
## 72 Proteasome PM 2
## 73 Proteasome Ribosome 40S 1
## 74 Proteasome Ribosome 60S 2
## 75 Proteasome unknown 1
## 79 Ribosome 40S Golgi 2
## 80 Ribosome 40S mitochondrion 1
## 84 Ribosome 40S Proteasome 1
## 85 Ribosome 40S Ribosome 60S 2
## 87 Ribosome 40S Cytoskeleton 1
## 95 Ribosome 60S Proteasome 2
## 103 unknown Nucleus 1
## 105 unknown PM 1
## 106 unknown Proteasome 2
## 109 unknown Cytoskeleton 17 Session information
All software and respective versions used to produce this document are listed below.
sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] viridis_0.6.2 viridisLite_0.4.0 pheatmap_1.0.12
## [4] pRolocdata_1.32.0 ggplot2_3.3.5 bandle_1.0
## [7] pRoloc_1.34.0 BiocParallel_1.28.3 MLInterfaces_1.74.0
## [10] cluster_2.1.2 annotate_1.72.0 XML_3.99-0.8
## [13] AnnotationDbi_1.56.2 IRanges_2.28.0 MSnbase_2.20.1
## [16] ProtGenerics_1.26.0 mzR_2.28.0 Rcpp_1.0.7
## [19] Biobase_2.54.0 S4Vectors_0.32.3 BiocGenerics_0.40.0
## [22] BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.13 BiocFileCache_2.2.0 plyr_1.8.6
## [4] splines_4.1.2 listenv_0.8.0 GenomeInfoDb_1.30.0
## [7] digest_0.6.29 foreach_1.5.1 htmltools_0.5.2
## [10] magick_2.7.3 gdata_2.18.0 ggalluvial_0.12.3
## [13] fansi_0.5.0 magrittr_2.0.1 memoise_2.0.1
## [16] doParallel_1.0.16 mixtools_1.2.0 limma_3.50.0
## [19] recipes_0.1.17 globals_0.14.0 Biostrings_2.62.0
## [22] gower_0.2.2 lpSolve_5.6.15 prettyunits_1.1.1
## [25] colorspace_2.0-2 ggrepel_0.9.1 blob_1.2.2
## [28] rappdirs_0.3.3 xfun_0.28 dplyr_1.0.7
## [31] crayon_1.4.2 RCurl_1.98-1.5 jsonlite_1.7.2
## [34] hexbin_1.28.2 impute_1.68.0 survival_3.2-13
## [37] iterators_1.0.13 glue_1.5.1 gtable_0.3.0
## [40] ipred_0.9-12 zlibbioc_1.40.0 XVector_0.34.0
## [43] kernlab_0.9-29 shape_1.4.6 future.apply_1.8.1
## [46] scales_1.1.1 vsn_3.62.0 mvtnorm_1.1-3
## [49] DBI_1.1.1 xtable_1.8-4 progress_1.2.2
## [52] clue_0.3-60 bit_4.0.4 proxy_0.4-26
## [55] mclust_5.4.8 preprocessCore_1.56.0 lbfgs_1.2.1
## [58] MsCoreUtils_1.6.0 lava_1.6.10 prodlim_2019.11.13
## [61] sampling_2.9 httr_1.4.2 FNN_1.1.3
## [64] RColorBrewer_1.1-2 ellipsis_0.3.2 farver_2.1.0
## [67] pkgconfig_2.0.3 nnet_7.3-16 sass_0.4.0
## [70] dbplyr_2.1.1 utf8_1.2.2 caret_6.0-90
## [73] reshape2_1.4.4 tidyselect_1.1.1 rlang_0.4.12
## [76] munsell_0.5.0 tools_4.1.2 LaplacesDemon_16.1.6
## [79] cachem_1.0.6 generics_0.1.1 RSQLite_2.2.9
## [82] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0
## [85] mzID_1.32.0 yaml_2.2.1 ModelMetrics_1.2.2.2
## [88] knitr_1.36 bit64_4.0.5 randomForest_4.6-14
## [91] purrr_0.3.4 KEGGREST_1.34.0 dendextend_1.15.2
## [94] ncdf4_1.18 future_1.23.0 nlme_3.1-153
## [97] xml2_1.3.3 biomaRt_2.50.1 compiler_4.1.2
## [100] filelock_1.0.2 curl_4.3.2 png_0.1-7
## [103] e1071_1.7-9 affyio_1.64.0 tibble_3.1.6
## [106] bslib_0.3.1 stringi_1.7.6 highr_0.9
## [109] lattice_0.20-45 Matrix_1.4-0 vctrs_0.3.8
## [112] pillar_1.6.4 lifecycle_1.0.1 BiocManager_1.30.16
## [115] GlobalOptions_0.1.2 jquerylib_0.1.4 MALDIquant_1.20
## [118] data.table_1.14.2 bitops_1.0-7 R6_2.5.1
## [121] pcaMethods_1.86.0 affy_1.72.0 bookdown_0.24
## [124] gridExtra_2.3 parallelly_1.29.0 codetools_0.2-18
## [127] gtools_3.9.2 MASS_7.3-54 assertthat_0.2.1
## [130] withr_2.4.3 GenomeInfoDbData_1.2.7 parallel_4.1.2
## [133] hms_1.1.1 grid_4.1.2 rpart_4.1-15
## [136] timeDate_3043.102 tidyr_1.1.4 coda_0.19-4
## [139] class_7.3-19 rmarkdown_2.11 segmented_1.3-4
## [142] pROC_1.18.0 lubridate_1.8.0